All present day telecommunication systems are dependent on accurate measurements of various parameters over a geographical area in order to provide ample coverage and optimal transmission paths over that area. The measurements are usually collected at various geographical locations, typically represented by discrete points (X; Y; Z) where X and Y typically denotes the 2-D position and Z any measurable value at the point. Among the different parameters to be measured are signal strength, altitude, radio characteristics, geographical characteristics etc. However, also other parameters are plausible.
In order to be able to perform efficient analyses based on the measured data from the various discrete points, it is often desired and necessary to convert the data into a regular grid or raster.
Several known methods exist to enable interpolation of irregularly scattered data points into a regular raster or grid. All methods have their advantages and disadvantages. Some of those interpolation methods are considered to be more exact, in the sense that they honor the original sampled points upon which the interpolation is based. Different methods can also produce a more or less smoothing effect on the result.
Some of the most well known methods for point to area interpolations are the Triangulated Irregular Network (TIN), Kriging, polynomial regression, and natural regions. The natural regions method is also known as Voronoi polygons, or Thiessen polygons, and is mostly used for gridding of qualitative data points, since no new point values are computed. The other three methods will interpolate new values to fill the unsampled points. TIN and Kriging can be classified as exact methods, although it may not always be the truth. Polynomial regression is a method that among some is most favoured, since it is possible to include more parameters in the interpolation than only the position of the sampled points. In its most simple form polynomial regression is also referred to as trend analysis, all sampled points, or a sub-set of them, are used to compute a best fitting surface of one to three dimensions. Trend analysis is usually used as a global method of interpolation, to find underlying trends of a data set and remove them before further processing of the data.
Most existing methods for converting randomly distributed data into a regular grid have problems with large amounts of data, i.e. the time and memory required for the conversion does not grow linearly with the number of data point, but rather exponentially or worse. Therefore, there is a need for a method of converting large amount of randomly geographically distributed data into a regular grid or raster to enable more efficient radio network planning.